Will the 2016 Records be Statistically Significant? (Now Includes October and November Data)

HadCRUT4, Hadsst3 and GISS have 2015 as the record warm year to this point. The above graphic has been set so that all January 2016 values meet at the same point. In order for 2016 to set a record, then the meeting point, as well as all points to the right, need to have a greater height than all points to the left. Viewing all points, it appears that 2016 is higher than 2015 and thus should set a record barring a huge drop through the end of the year

.

In this article I will provide specific numbers for these three data sets and what is required for the remainder of the year in order to set a record. Then I will provide the appropriate information for the two satellite data sets where the previous record was 1998.

The title of this article refers to statistical significance of these potential records. For this purpose, I will assume that if the 2016 average is 0.1 above the previous record, the result is statistically significant in the sense that there is a 95% chance there is indeed a new record. A difference of less than 0.1 indicates a statistical tie for two years, even though one anomaly may be higher than the other. For example, there may be only a 70% chance of 2016 being a record.

The third place year could also be important if it is within 0.1 of the record year. For example, if the new record is 0.02 C above the old one, but two years are 0.02 C lower, then you could have something like a 40% chance that 2016 is a record but that each of the other two years has a 30% chance of being the record holder. Below, I will provide the following information for the five data sets I track.

1. The record high anomaly

2. The average to date

3. What is needed in the remaining month or two to set a new record

4. How low the third place year is and its affect on a new record

5. My prediction for the statistical significance for a new record

UAH

The record high anomaly was in 1998 with an anomaly of 0.484. The average for the first eleven months of this year is 0.529 which means that the December anomaly needs to be -0.011 or a huge drop from 0.45 in November to prevent a new record. The third place year would be 2010 where the anomaly was 0.338 so it will have no affect. So while the chances are over 50% that 2016 will set a new record, it will be in a statistical tie with 1998.

RSS

The record high anomaly was in 1998 with an anomaly of 0.550. The average for the first eleven months of this year is 0.604 which means that the December anomaly needs to be -0.044 or a huge drop from 0.390 in November to prevent a new record. The third place year would be 2010 where the anomaly was 0.475 so it will have no affect. So while the chances are over 50% that 2016 will set a new record, it will be in a statistical tie with 1998.

HadCRUT4.5

The record high anomaly was in 2015 with an anomaly of 0.760. The average for the first ten months of this year is 0.816 which means that the next two months need to average 0.48 or a small drop from 0.587 in October to prevent a new record. So the red line above has to go down an average of 0.107 over the next two months. The third place year would be 2014 where the anomaly was 0.575 so it will have no affect. HadCRUT4.5 could be close!

Hadsst3

The record high anomaly was in 2015 with an anomaly of 0.592. The average for the first ten months of this year is 0.641 which means that the next two months need to average 0.347 or a huge drop from 0.603 in October to prevent a new record. So the green line above has to go down an average of 0.256 over the next two months. The third place year would be 2014 where the anomaly was 0.477 so it will have no affect. So while the chances are over 50% that 2016 will set a new record, it will be in a statistical tie with 1998.

GISS

The record high anomaly was in 2015 with an anomaly of 0.87. The average for the first ten months of this year is 1.02 which means that the next two months need to average 0.12 or a huge drop from 0.89 in October to prevent a new record. So the blue line above has to go down an average of 0.90 over the next two months, which cannot happen. The third place year would be 2014 where the anomaly was 0.75 so it will have no affect. As a matter of fact, in order to fall to 0.97, the next two months need to average 0.72 which is also a relatively large drop from 0.89 in October. In my opinion, the chances are pretty good that GISS will set a record in 2016 that will be statistically significant.

In the sections below, we will present you with the latest facts. The information will be presented in two sections and an appendix. The first section will show for how long there has been no statistically significant warming on several data sets. The second section will show how 2016 so far compares with 2015 and the warmest years and months on record so far. For three of the data sets, 2015 also happens to be the warmest year. The appendix will illustrate sections 1 and 2 in a different way. Graphs and a table will be used to illustrate the data. Only the satellite data go to November.

Section 1

For this analysis, data was retrieved from Nick Stokes’ Trendviewer available on his website. This analysis indicates for how long there has not been statistically significant warming according to Nick’s criteria. Data go to their latest update for each set. In every case, note that the lower error bar is negative so a slope of 0 cannot be ruled out from the month indicated.

On several different data sets, there has been no statistically significant warming for between 0 and 23 years according to Nick’s criteria. Cl stands for the confidence limits at the 95% level.

The details for several sets are below.

For UAH6.0: Since October 1993: Cl from -0.011 to 1.810

This is 23 years and 2 months.

For RSS: Since July 1994: Cl from -0.000 to 1.785 This is 22 years and 5 months.

For Hadcrut4.5: The warming is statistically significant for all periods above three years.

For Hadsst3: Since February 1997: Cl from -0.008 to 2.136 This is 19 years and 9 months.

For GISS: The warming is statistically significant for all periods above three years.

Section 2

This section shows data about 2016 and other information in the form of a table. The table shows the five data sources along the top and other places so they should be visible at all times. The sources are UAH, RSS, Hadcrut4, Hadsst3, and GISS.

Down the column, are the following:

1. 15ra: This is the final ranking for 2015 on each data set.

2. 15a: Here I give the average anomaly for 2015.

3. year: This indicates the warmest year on record so far for that particular data set. Note that the satellite data sets have 1998 as the warmest year and the others have 2015 as the warmest year.

4. ano: This is the average of the monthly anomalies of the warmest year just above.

5. mon: This is the month where that particular data set showed the highest anomaly prior to 2016. The months are identified by the first three letters of the month and the last two numbers of the year.

6. ano: This is the anomaly of the month just above.

7. sig: This the first month for which warming is not statistically significant according to Nick’s criteria. The first three letters of the month are followed by the last two numbers of the year.

8. sy/m: This is the years and months for row 7.

9. Jan: This is the January 2016 anomaly for that particular data set.

10. Feb: This is the February 2016 anomaly for that particular data set, etc.

20. ave: This is the average anomaly of all months to date.

21. rnk: This is the rank that each particular data set would have for 2016 without regards to error bars and assuming no changes to the current average anomaly. Think of it as an update 55 minutes into a game.

Source

UAH

RSS

Had4

Sst3

GISS

1.15ra

3rd

3rd

1st

1st

1st

2.15a

0.261

0.381

0.760

0.592

0.87

3.year

1998

1998

2015

2015

2015

4.ano

0.484

0.550

0.760

0.592

0.87

5.mon

Apr98

Apr98

Dec15

Sep15

Dec15

6.ano

0.743

0.857

1.024

0.725

1.11

7.sig

Oct93

Jul94

Feb97

8.sy/m

23/2

22/5

19/9

Source

UAH

RSS

Had4

Sst3

GISS

9.Jan

0.540

0.680

0.906

0.732

1.16

10.Feb

0.831

0.993

1.068

0.611

1.34

11.Mar

0.733

0.870

1.069

0.690

1.30

12.Apr

0.714

0.784

0.915

0.654

1.09

13.May

0.544

0.542

0.688

0.595

0.94

14.Jun

0.338

0.485

0.731

0.622

0.76

15.Jul

0.388

0.492

0.728

0.670

0.84

16.Aug

0.434

0.471

0.770

0.654

0.99

17.Sep

0.441

0.580

0.712

0.606

0.90

18.Oct

0.408

0.353

0.587

0.603

0.89

19.Nov

0.45

0.390

20.ave

0.529

0.604

0.816

0.641

1.02

21.rnk

1st

1st

1st

1st

1st

Source

UAH

RSS

Had4

Sst3

GISS

If you wish to verify all of the latest anomalies, go to the following:

To see all points since January 2016 in the form of a graph, see the WFT graph below.

WoodForTrees.org – Paul Clark – Click the pic to view at source

As you can see, all lines have been offset so they all start at the same place in January 2016. This makes it easy to compare January 2016 with the latest anomaly.

The thick double line is the WTI which shows the average of RSS, UAH6.0beta5, HadCRUT4.5 and GISS.

Appendix

In this part, we are summarizing data for each set separately.

UAH6.0beta5

For UAH: There is no statistically significant warming since October 1993: Cl from -0.011 to 1.810. (This is using version 6.0 according to Nick’s program.)

The UAH average anomaly so far for 2016 is 0.529. This would set a record if it stayed this way. 1998 was the warmest at 0.484. Prior to 2016, the highest ever monthly anomaly was in April of 1998 when it reached 0.743. The average anomaly in 2015 was 0.261 and it was ranked 3rd.

RSS

Presently, for RSS: There is no statistically significant warming since July 1994: Cl from -0.000 to 1.785.

The RSS average anomaly so far for 2016 is 0.604. This would set a record if it stayed this way. 1998 was the warmest at 0.550. Prior to 2016, the highest ever monthly anomaly was in April of 1998 when it reached 0.857. The average anomaly in 2015 was 0.381 and it was ranked 3rd.

Hadcrut4.5

For Hadcrut4.5: The warming is significant for all periods above three years.

The Hadcrut4.5 average anomaly so far is 0.816. This would set a record if it stayed this way. Prior to 2016, the highest ever monthly anomaly was in December of 2015 when it reached 1.024. The average anomaly in 2015 was 0.760 and this set a new record.

Hadsst3

For Hadsst3: There is no statistically significant warming since February 1997: Cl from -0.008 to 2.136.

The Hadsst3 average anomaly so far for 2016 is 0.641. This would set a record if it stayed this way. Prior to 2016, the highest ever monthly anomaly was in September of 2015 when it reached 0.725. The average anomaly in 2015 was 0.592 and this set a new record.

GISS

For GISS: The warming is significant for all periods above three years.

The GISS average anomaly so far for 2016 is 1.02. This would set a record if it stayed this way. Prior to 2016, the highest ever monthly anomaly was in December of 2015 when it reached 1.11. The average anomaly in 2015 was 0.87 and it set a new record.

Conclusion

Does it seem odd that GISS will probably set a statistically significant record in 2016 whereas HadCRUT4.5 may keep its 2015 record?

83 thoughts on “Will the 2016 Records be Statistically Significant? (Now Includes October and November Data)”

Thank you. However when giving statements like this, you should always say where you are from. Here in Edmonton, Alberta, Canada, it is -15 C right now. And our government wants to impose a carbon tax on January 1!

Funny you should mention that, because I’m just a little west of Edmonton. I gave the temperature in Fahrenheit because most people on this site seem to measure their temperature that way. Yes of course, I know about the carbon tax, and I don’t like it.

The temperature at my house right now is totally relevant. And if I am traveling, the destination temperature is totally relevant. If I walk out of the house when it is 0C and I’m dressed like it is 35C, it’s relevant.
Because it is all about me, not you.

Yeah and its been about 100F in Chad for the last 100 years. I was in Northern Nigeria – near Chad in mid 1960s and both in the far north, and in Lagos on the coast, their respective temperatures were about the same as they are now. I suspect Chad may be marginally cooler since their has been considerable greening of late with the high CO2. Also, Dave, If we have had such severe warming since 1980, then there would be no way of telling this from Edmonton’s temperature today. Would you say that if it is still this cold in 2050 that global warming may not be such a problem. I think this is why CAGW is being clung to by “progressives” and their useful idiots. They can’t even let go of the election results in the US. Anyway, your children/grandchildren will be getting a different education to yours in the near future.

There is a lot of truth to this. But as long as countries want to spend billions to fight it, we have to do what we can. I am very glad Trump got in. I am just not happy that Harper is out in Canada since he knew it was a hoax. Furthermore, he said he would follow the United States in this matter. That would have been great under Trump! But now Trudeau is in and he will go ahead with carbon taxes, regardless what Trump does.

“…As for 100F I don’t like that either.”
That’s less that 38 degrees centigrade. It frequently goes over 40 degrees in Canberra in summer. I used to go jogging at 43 degrees; it’s all what you’re acclimatized to.

Werner, not quite sure why you used the Jan 2016 GISS value as the basis for applying offsets in figure 1: I’m not at all sure its a good idea to use any single monthly value as the basis for this (although I take your point that you are comparing 2 adjacent years here). But wouldn’t it make sense then to offset all 3 data sets to the average for 2015? Then it would be easier to see that the average of the 2016 months would need to be more than zero?

“do something like Nick Stokes does on his site”
What, like write some kind of deranged theology and burble fulsomely on about spectral analysis in GCMs and Lorenz attractors while ignoring the completely unsupported inbuilt fundamental positive feedbacks and the fact that the models demonstrate zero predictive skill? Then post a handful of comments which were either auto generated or written by his mom? First time there thanks to your post and wow but that puppy is badly in need of help.

cephus0 on December 5, 2016 at 8:37 am
What’s that for a stupid, arrogant comment, above all comfortably written behind a nick name?
Who are you? What did you reach in your life? Tell us evverything, cephus0.
Becaus up to now, you behave justlike no more than a bare zero!

Literally mad. It’s as though he thought if he got all of his sciencey sounding toys out then somehow it would all be real. Could not have been more surprised had he hypothesised that unicorn horns are made out of a keratin / carbon nanotube composite and started doing a fracture mechanics and failure mode analysis in order to bypass the fact that he has yet to show that they exist at all.
@ Bindidon: you really don’t want to know what my qualifications are but I won’t be carrying out an argument from authority here so I won’t be engaging with you on that but will instead leave you to faun at Stokes’ knees like the halfwit you are.

cephus0 on December 5, 2016 at 9:02 am… you really don’t want to know what my qualifications are…
Indeed I don’t, and the very first reason is that you do nothing to show anything about them.
What you show here is the classical example of an incredibly impolied and arrogant person.
Be happy that Anthony’s moderation is so tolerant; in France, or in Germany where I live, one single comment like yours is immediately dropped off the thread, and one more of that kind bans you off the site for years.
Luckily, you belong to a thoroughly insignificant minority. Und das ist gut so !!!

That’s about as polite as it gets when presented with screeds of gratuitous irrelevant theology I’m afraid. And I’m well aware of what you and your ilk would have done with any dissenting voice thanks all the same. We’ve all been watching it in action for many years now. Mr. Watts on the other hand appears not to have anything to fear from dissent or honestly expressed opinion. How odd.

Dave is right, that’s evident. Here is for example a chart with plots of the monthly mean of the accumulated anomaly differences, for 2 TLT and 3 surf time series:http://fs5.directupload.net/images/161205/oedfgcga.jpg
And here we see what’s important for each series: 2016 beats 2015 if and only if the plots ends keep above zero when rtheir november and december anomailes are in.

2016 has had Brexit, Trump + record drop in RSS, and whilst 2016 may be a record, with merely days till Trump’s inauguration, there will be hardly time for them to celebrate all the warming until they are back to squealing as the axe falls.

This statement needs some further explanation at this point. The title: “Steepest drop in global temperature on record” was misleading since it only talked about land temperatures which is only about 30% of global. Furthermore, the land temperature jumped by 0.225 from October to November on RSS so it is dated.

This is a horse race and a boring one at that. What is interesting is how the horses have been doped up. There are other horse races, Satellite vs. tide gauges, and the same doping goes on. And the sea ice saga continues and it’s a good bet that doctored data is there as well although any manifestation is not clear. Other dog and pony shows; tornado counts, hurricane land falls, wild fires, etc. are even more boring except for the skullduggery that goes on.
Well the United States of America will soon be under a new management. I look forward to an investigation of the above issues as only part of a roll back of unnecessary regulation and raw political bullying.

AZ1971 on December 5, 2016 at 7:18 amAlarmists will refer to any data source that provides the maximum fear in their rhetoric…
Ha! You make me lucky today evening. Because according to your excellent definition, I’m not a warmist. If namely I was one of these strange persons, I wouldn’t use such a cool temperature series like GISS!
I would use the much warmer GHCN unadjusted record instead 🙂

Thanks for the update Werner and everyone.
2016 is a year that came in hot from the high solar max TSI in 2015 that drove the recent ENSO, and is going out colder now due to this year’s lower TSI and ongoing ENSO OHC depletion.
In Dec of 2015 I expected a solar slowdown this year and a subsequent temperature drop-off from it. Because it occurred before under similar circumstances, I predicted then that this year would not be a record year in (SST), a prediction that may yet be close yet still be wrong.
Unless we see a horrific December deep-freeze, that prediction won’t likely pan out in spite of the already record temperature drop-off this year, as most of the sun’s TSI slowdown for this year happened early in the year and has since flattened out for months, with OHC tracking up/down from it fairly closely since then.

My best guess is that because the ice extent is much below average at both poles, the air above the greatly exposed water regions cannot sink as low as when things are ice covered. That could be part of it.

Bob Weber on December 5, 2016 at 7:36 am2016 is a year that came in hot from the high solar max TSI in 2015 that drove the recent ENSO
I would be happy if you could manage to cite a source confirming your assumptions that
– 2015 had a “high solar max TSI”;
– a year with a “high solar max TSI” „comes in hot“;
– a “high solar max TSI” drives ENSO.
You see a chart comparing yearly plots for TSI (spot.colorado.edu), Silso’s SSN and MEI:http://fs5.directupload.net/images/161205/zi553763.jpg
Below is the sorted list of the yearly TSI values, reduced to the strongest ENSO start years:
1. 1981 1361.68
…
11. 2015 1361.43
…
24. 1997 1360.81
…
33. 1986 1360.64
Excepted for 2015, I don’t see any real correlation between solar activity (TSI, SSN) and ENSO peaks.

There’s a lot more to it than that brief look. My research results are not assumptions, but results that correspond to what I call solar super sensitivity rules, that I derived empirically from solar and ocean data. I aim to encapsulate it all in an article so you etal can understand it, which should be easy for you.
Perhaps you weren’t following very closely to what I was saying during the past two plus years here, where I regularly described the solar cause of the ENSO before and as it happened, and that I determined the solar SST-TSI warming/cooling threshold value of 1361.25 w/m^2 in 2015, which was verified this year in March as TSI dropped below that value, where it has stayed for all but a few days this year to date, by a perfectly corresponding drop from warm to cool in equatorial ocean heat content and sea surface temps:http://www.ospo.noaa.gov/data/sst/anomaly/2015/anomnight.12.3.2015.gifhttp://www.ospo.noaa.gov/data/sst/anomaly/2016/anomnight.12.5.2016.gifhttp://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_update/heat-last-year.gif
I recommend that you check out David Stockwell’ s papers on accumulation and supersensivity, at Niche Modelling. ENSOs typically dissipate within 2-3 years max, within his statistically determined lag, which is what we’re seeing now.
There’s so much more to this that it deserves more, but for now I hope you will take away the appreciation that we’re just past a seven year TSI rise that ended last year, that TSI has tanked this year, but is now holding steady enough to not change things very quickly:
Year 1au TSI
2015 1361.4321
2014 1361.3966
2013 1361.3587
2012 1361.2413
2011 1361.0752
2003 1361.0292
2016 1361.0265
2004 1360.9192
2010 1360.8027
2005 1360.7518
2006 1360.6735
2007 1360.5710
2009 1360.5565
2008 1360.5382

My comment was prematurely posted under my WP name.
TSI crashed in 2016 taking temps with it.
TSI has already dropped over 0.4 w/m^2 this year, one of the top five annual drop-offs since 1979 (ACRIM & SORCE data).

Bob Weber on December 5, 2016 at 12:58 pmTSI has already dropped over 0.4 w/m^2 this year, one of the top five annual drop-offs since 1979 (ACRIM & SORCE data).
In 1982, 1989 and 2003 (-0.61) the TSI shifts up or down were quite a bit higher than from january till november 2016 (-0.39).
And what in the world did happen these years?
No idea about what you are telling here… pure alarmism.
Excuse me: what counts here is the long term.

From the article: “it appears that 2016 is higher than 2015 and thus should set a record barring a huge drop through the end of the year”
Should read: “According to the bastardized surface charts” it appears that 2016 is higher than 2015 and thus should set a record barring a huge drop through the end of the year.”
According to the UAH satellite chart, the highpoint of 1998 was hotter than any subsequent point up to Feb. 2016, when the highpoint was exceeded by one-tenth of a degree. 2015 is an also-ran on the UAH chart as are all years in between 1998 and Feb. 2016. Perspective is what we need.
So, as far as I’m concerned using the surface charts is a waste of time. Those who do are speculating using manipulated/biased data as the basis for their conclusions.

TA: “Perspective is what we need.”
Bindidon December 5, 2016 at 12:24 pm wrote: “Yes, TA, indeed. And here is the perspective I guess you might need, namely a sorted list of UAH6.0beta5’s ten “hottest months evah” for the Globe:”
Thanks for that, Bindidon. Dr. Spencer estimates 2016 may be 0.03-0.04C hotter than 1998, which means they are statistically tied for first place in the satellite record. Do those numbers you provided change that fact any? Anwer: No.

Since the people trying to justify their budgets for monitoring global warming are allowed to adjust the temperatures in these data sets I don’t know what you can conclude from them. Can you imagine if professional baseball players were allowed to adjust their statistics based on factors they thought were relevant? This at bat would have been a hit if I didn’t have an injured thumb, or the wind was unusually high that day or this at bat would have been a home run… do you think they would ever adjust their batting averages down? Its the same thing to me with temperature data, it is a measure of the importance of global warming, because the more the warming trend the greater the urgency that can be claimed by those trying to justify their budgets for studying it, just as batting statistics are a measure of the importance of a player to the offensive capabilities of a baseball team.
Ten years from now if the sun’s reduced activity creates a cooling trend and no one cares about global warming anymore then instead of continually adjusting the temperature data to show more of a rise these same data set managers will start adjusting the data to show more of a cooling trend, doing it slowly with multiple change iterations so we barely notice the changes. The worse thing that can happen for managers trying to build an empire is for their product to lose relevance or necessity, so as long as we allow them to adjust their data they will always be trying to work any angle they can to show some climate issue that needs to be closely monitored.

Can you imagine if professional baseball players were allowed to adjust their statistics based on factors they thought were relevant?

Can you imagine who would be president if we went by the total popular vote? Some people think that rule should be changed too. As an aside, some people think Clinton should be president because she got 48% versus 47% for Trump. But neither got 50%!

Its impossible to know who would be President if it went by popular vote – because the election would have been utterly different.
For example, i doubt that the Republicans spent much money in California as they were never going to win it, and it’s possible that the relatively low turnout in California reflects Republicans who thought their vote wouldn’t matter.
But if the election was by popular vote, the Republicans would have spent their money differently and turnout might have been higher. If say 5% of the Californian electorate didn’t vote but would have voted Republican, then that would take Trump over the line in the popular vote. Of course the reverse may be true elsewhere, but California is disproportionately large in the popular vote compared to the electoral college vote.

“He who knows only his own side of the case knows little of that. His reasons may be good, and no one may have been able to refute them. But if he is equally unable to refute the reasons on the opposite side, *if he does not so much as know what they are*, he has no ground for preferring either opinion… Nor is it enough that he should hear the opinions of adversaries from his own teachers, presented as they state them, and accompanied by what they offer as refutations. He must be able to hear them from persons who actually believe them…he must know them in their most plausible and persuasive form.” ― John Stuart Mill, On Liberty
Those who only seek out commentators and blogs that confirm their beliefs are not being scientific; they are being religious.
And that goes for people on *both* sides of a question.

Brozek should tell anyone who thinks Clinton should be president because the electoral system didn’t produce the result they wanted that if they start tampering with the process just because they don’t like the result that the end of that lies with what happened in Germany in the nineteen thirties – a one party state intolerant of all opposition (and also the “greenest” government that Europe had seen. It was just “other” people the Nazis hated – sounds sort of familiar.)

Perhaps you should read the constitution. It states that:
“The Electors shall meet in their respective States, and vote by Ballot for two Persons, of whom one at least shall not be an Inhabitant of the same State with themselves. And they shall make a List of all the Persons voted for, and of the Number of Votes for each; which List they shall sign and certify, and transmit sealed to the Seat of the Government of the United States, directed to the President of the Senate.”
Note that there is nothing there about how the electors should chose who to vote for. Thus it would be
completely constitutional for the electors to choose Clinton on the grounds that she received more votes.
Alternatively they could choose a moderate republican or vote for Trump. The current system whereby
electors generally give all of their votes to who-ever got the most votes in that state is just one option and
has no constitutional basis. It can be changed overnight.
It should be noted that a number of states currently have laws stating that their electors should vote for
who-ever received the most votes but with the proviso that the law will only come into effect once a majority
of states have the same law.

“I will assume that if the 2016 average is 0.1 above the previous record, the result is statistically significant in the sense that there is a 95% chance there is indeed a new record. “
What? It seems you are arbitrarily deciding what is “statistically significant” (“0.1 K”), rather than calculating it using some sort of statistical rule (eg T-test).

UPDATE: It should be pointed out that 2016 will end up being 0.03-0.04 deg. C warmer than 1998, which is probably not a statistically significant difference given the uncertainties in the satellite dataset adjustments.

I recall reading in an earlier post that we should only expect the UAH yearly average to be within 0.1 of the true value. For another data set, I recall reading that the yearly average is within 0.09. Either way, my comments about the approximate probabilities for statistical ties are still valid in either case.

All of this will seem academic should the pause reappear in the run up to AR6.
There is nothing significant about a short lived high, or a short lived low. It matters not that 2016 is or is not warmer than 1998. Both 1997/98 and 2015/16 were strong El Nino years. It appears that all we are looking at is whether one of them was a stronger El Nino than the other. Since both are natural events, there is not much significance in that.
The significant issue is that coincident upon the 1997/98 Super EL Nino there was a long lasting step change in temperature of about 0.26degC. The reason for this is not known, Unless there is a similar long lasting step change in temperature coincident with the 2015/16 El Nino, the 2015/16 El Nino although a strong one will be seen to be a damp squib.
The present El Nino cycle has yet to complete. Whilst La Nina events appear to have less impact than El Ninos, let’s see what the next 18 months/2 years have in store.

Werner Brozek, you say:
“Does it seem odd that GISS will probably set a statistically significant record in 2016 whereas HadCRUT4.5 may keep its 2015 record?”
I’m no expert on statistics, but from what I recall, statistical significance is purely determined by the values in the data set and does not take into account the accuracy or uncertainty of the individual measurements or in this case, the individual estimates. I’ve worked with temperature analyses and QA/QC for over 40 years now and I would subjectively estimate that monthly and annual estimates of global surface temperature anomalies (GSTA) are probably not accurate to more than about plus or minus 0.3C to 0.5C at best since 1979 and possibly worse. Prior to the satellite/ocean buoy era the GSTA estimates are probably much less accurate and may not be much better than guesstimates prior to around 1900.

Showing all this data to three significant figures is misleading without also showing error bars. It would be better to report that the earth’s average temperature over the period 1950-80 was 14 degrees C according to NOAA. Today the average temperature is about 15 degrees C. Problem anyone?

Showing all this data to three significant figures is misleading without also showing error bars.

I am reporting what they say. If you have an issue with how they report things, please take it up with them. However I find your comment rather ironic since my post was about how inaccurate the yearly averages are.

Forrest Gardener on December 5, 2016 at 7:16 amWhere do these two graphs fit in?
In fact they hardly could: simply because Werner Brozek’s intention is not to show us how satellite readings react to an ENSO transition.
What he intends is to check wether or not 2016 will bypass 2015. And that does not depend on how steep satellite readings are able to drop up or down, but on the comparison between their means for the two years.
The actual 2016/2015 mean comparison for the period January-October:
– UAH: 0.54 / 0.26 °C
– RSS: 0.53 / 0.26 °C
– GISS: 0.60 / 0.40 °C
– NOAA: 0.53 / 0.44 °C
– HadCRUT4: 0.52 / 0.43 °C
It is somewhat unlikely to see the 2015 horse ending this year in front.

A record low or high temperature for 1 to 2 years is weather variation, not climate variation. The emphasis that both side seem to be making about this short term effect is nonsense. If in 4 to 5 more years (still a limited period to show true trends) the averaged trend continues, but at at a slope of <1C per century, the only conclusion is that the models are clearly wrong (as is already obvious). A temporary peak, caused by El Nino, on a local slightly high plateau being touted as a meaningful is no more meaningful than a large volcano causing a temporary dip.

Thank you! Would you have all yearly averages? It is nice to see that my graph is on a Japanese site!
According to my calculations, November and December for 2016 could actually go up to 0.389 from 0.30 in October in order to tie 2015. I will be watching for the next anomalies and perhaps use them in my next report.

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